Time domain graph-based anomaly detection approach applied to a real industrial problem

Alvarenga, Wagner J.; Campos, Felipe V.; Costa, Alexsander C.A.A.; Salis, Turibio T.; Magalhães, Eduardo; Torres, Luiz C. B.; Braga, Antônio de Pádua

doi:10.1016/j.compind.2022.103714

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The present work demonstrates that the proposed models, such as Isolation Forest, RNNs, and VAEs, can effectively identify anomalies in urban data, such as unusual fluctuations in electrical energy demand, voltage manipulation attempts, the injection of false data, and frequency alterations. These findings support the usefulness of anomaly detection models in monitoring critical systems in smart cities, such as the power grid [37].…”

Section: Discussionsupporting

confidence: 72%

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.

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Section: Discussionsupporting

confidence: 72%

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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“…Industrial anomaly detection is vital in ensuring industrial processes' reliability, safety, and efficiency. In this subsection, we review recent research efforts in industrial anomaly detection, including product quality inspection, system state monitoring, equipment failure detection, and other industrial applications [15][16][17][18][19][20][21][22][23].…”

Section: Industrial Anomaly Detectionmentioning

confidence: 99%

“…MSTUnet combines the swin transformer and Unet networks, demonstrating superior performance in anomaly detection and localization on industrial datasets. Alvarenga et al [17] propose a graph-based approach to anomaly detection, leveraging graph theory and set coverage principles. This method achieves results comparable to deep autoencoders without the need for extensive parameter tuning.…”

Section: Industrial Anomaly Detectionmentioning

confidence: 99%

Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

Qiu,

Shi,

et al. 2023

Applied Sciences

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Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.

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Interpretable temporal degradation state chain based fusion graph for intelligent bearing fault detection

Xia,

Xing,

Yan

et al. 2024

Advanced Engineering Informatics

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Time domain graph-based anomaly detection approach applied to a real industrial problem

Cited by 7 publications

References 25 publications

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

Interpretable temporal degradation state chain based fusion graph for intelligent bearing fault detection

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